Determining tissue oxygen saturation with melanin correction

ABSTRACT

An oximeter probe that takes into account tissue color (e.g., skin color or melanin content) to improve accuracy when determining oxygen saturation of tissue. Light is transmitted from a light source into tissue having melanin (e.g., eumelanin or pheomelanin). Light reflected from the tissue is received by a detector. A compensation factor is determined to account for absorption due to the melanin. The oximeter uses this compensation factor and determines a melanin-corrected oxygen saturation value.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.15/494,444, filed Apr. 21, 2017, issued as U.S. Pat. No. 10,820,863 onNov. 3, 2020, which claims the benefit of the following U.S. patentapplications 62/325,919, filed Apr. 21, 2016, 62/326,630, 62/326,644,and 62/326,673, filed Apr. 22, 2016. These applications and U.S. patentapplication 62/363,562, filed Jul. 18, 2016, are incorporated byreference along with all other references cited in these applications.

BACKGROUND OF THE INVENTION

The present invention relates generally to optical systems that monitoroxygen levels in tissue. More specifically, the present inventionrelates to optical probes, such as oximeters, that include sources anddetectors on sensor heads of the optical probes and that use locallystored simulated reflectance curves for determining oxygen saturation oftissue.

Oximeters are medical devices used to measure oxygen saturation oftissue in humans and living things for various purposes. For example,oximeters are used for medical and diagnostic purposes in hospitals andother medical facilities (e.g., surgery, patient monitoring, orambulance or other mobile monitoring for, e.g., hypoxia); sports andathletics purposes at a sports arena (e.g., professional athletemonitoring); personal or at-home monitoring of individuals (e.g.,general health monitoring, or person training for a marathon); andveterinary purposes (e.g., animal monitoring).

Pulse oximeters and tissue oximeters are two types of oximeters thatoperate on different principles. A pulse oximeter requires a pulse inorder to function. A pulse oximeter typically measures the absorbance oflight due to pulsing arterial blood. In contrast, a tissue oximeter doesnot require a pulse in order to function, and can be used to make oxygensaturation measurements of a tissue flap that has been disconnected froma blood supply.

Human tissue, as an example, includes a variety of light-absorbingmolecules. Such chromophores include oxygenated hemoglobin, deoxygenatedhemoglobin, melanin, water, lipid, and cytochrome. Oxygenatedhemoglobin, deoxygenated hemoglobin, and melanin are the most dominantchromophores in tissue for much of the visible and near-infraredspectral range. Light absorption differs significantly for oxygenatedand deoxygenated hemoglobins at certain wavelengths of light. Tissueoximeters can measure oxygen levels in human tissue by exploiting theselight-absorption differences.

Despite the success of existing oximeters, there is a continuing desireto improve oximeters by, for example, improving measurement accuracy;reducing measurement time; lowering cost; reducing size, weight, or formfactor; reducing power consumption; and for other reasons, and anycombination of these measurements.

In particular, assessing a patient's oxygenation state, at both theregional and local level, is important as it is an indicator of thestate of the patient's local tissue health. Thus, oximeters are oftenused in clinical settings, such as during surgery and recovery, where itmay be suspected that the patient's tissue oxygenation state isunstable. For example, during surgery, oximeters should be able toquickly deliver accurate oxygen saturation measurements under a varietyof nonideal conditions. While existing oximeters have been sufficientfor post-operative tissue monitoring where absolute accuracy is notcritical and trending data alone is sufficient, accuracy is, however,required during surgery in which spot-checking can be used to determinewhether tissue might remain viable or needs to be removed.

Therefore, there is a need for improved tissue oximeter probes andmethods of making measurements using these probes.

BRIEF SUMMARY OF THE INVENTION

An oximeter probe that takes into account tissue color (e.g., skin coloror melanin content) to improve accuracy when determining oxygensaturation of tissue. Light is transmitted from a light source intotissue having melanin (e.g., eumelanin or pheomelanin). Light reflectedfrom the tissue is received by a detector. A compensation factor isdetermined to account for absorption due to the melanin. The oximeteruses this compensation factor and determines a melanin-corrected oxygensaturation value.

In an implementation, to calculate oxygen saturation, an oximeter probeutilizes a relatively large number of simulated reflectance curves toquickly determine the optical properties of tissue under investigation.The optical properties of the tissue allow for the further determinationof the oxygenated hemoglobin and deoxygenated hemoglobin concentrationsof the tissue as well as the oxygen saturation of the tissue.

In one implementation, the oximeter probe can measure oxygen saturationwithout requiring a pulse or heart beat. An oximeter probe of theinvention is applicable to many areas of medicine and surgery includingplastic surgery. The oximeter probe can make oxygen saturationmeasurements of tissue where there is no pulse. Such tissue may havebeen separated from the body (e.g., a flap) and will be transplanted toanother place in the body. Aspects of the invention may also beapplicable to a pulse oximeter. In contrast to an oximeter probe, apulse oximeter requires a pulse in order to function. A pulse oximetertypically measures the absorption of light due to the pulsing arterialblood.

Tissue oximeters can generate skewed oximetry measurements for tissueshaving different melanin content. In an implementation, the oximeterprobe can make oximetry measurements of tissue where concentrations frommelanin tend not to effect calculated relative oxygen saturationmeasurements. The oximeter probe exploits the relatively constant slopeof the absorption coefficients of melanin where the slope tends not tochange regardless of whether melanin content is tissue is relativelyhigh or relatively low. The oximeter probe uses a mathematicaldeterminative approach so that the melanin concentration contributionsto the determined relative oxygen saturation go to zero. Thereby, actualmelanin concentrations do not need to be determined to further determinerelative oxygen saturation of target tissue.

In an implementation, a method includes transmitting light from a lightsource of an oximeter probe into a first tissue at a first location tobe measured, where the first tissue comprises a first melanin component,and the first melanin component comprises at least one of eumelanin orpheomelanin; receiving light at a detector of the oximeter probe that isreflected by the first tissue in response to the transmitted light,where the received light comprises a first melanin absorption componentdue to the first melanin component; determining a melanin compensationcomponent for a melanin absorption component due to a melanin componentof tissue, where the melanin absorption component comprises the firstmelanin component and the melanin component comprises the first melanincomponent; and using the melanin compensation component, obtaining amelanin-corrected oxygen saturation value for the first tissue, wheremelanin-corrected oxygen saturation value accounts for the melaninabsorption component.

In an implementation, a method includes providing an oximeter devicecomprising a probe tip comprising source structures and detectorstructures, where the oximeter device will measure oxygen saturation ofa tissue comprising eumelanin and pheomelanin; providing to the oximeterdevice an indication of a skin color of the tissue to be measured; usingthe indication of a skin color to calculate the oxygen saturation of thetissue comprising eumelanin and pheomelanin to obtain amelanin-corrected oxygen saturation value; and displaying themelanin-corrected oxygen saturation value on a display.

In an implementation, a system includes an oximeter device that includesa probe tip that includes source structures and detector structures on adistal end of the device and includes a display proximal to the probetip. The oximeter device calculates a melanin-corrected oxygensaturation value, and displays the melanin-corrected oxygen saturationvalue on the display. The oximeter device is specially configured to usethe probe tip to make a first measurement and a second measurement todetermine the melanin-corrected oxygen saturation value and receivefirst information based on the first measurement of a first tissue at afirst location. The melanin-corrected oxygen saturation value isunavailable for display after the first measurement is made and beforethe second measurement is made. The oximeter device is speciallyconfigured to receive second information based on the second measurementof a second tissue at a second location where the second location isdifferent from the first location; use the first information and secondinformation to determine the melanin-corrected oxygen saturation value.The melanin-corrected oxygen saturation value takes into account melanincomponents of the first tissue and second tissue, and the melanincomponents comprise eumelanin and pheomelanin. And the oximeter deviceis configured to display the melanin-corrected oxygen saturation on thedisplay.

Other objects, features, and advantages of the present invention willbecome apparent upon consideration of the following detailed descriptionand the accompanying drawings, in which like reference designationsrepresent like features throughout the figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an oximeter probe in an implementation.

FIG. 2 shows an end view of the probe tip in an implementation.

FIG. 3 shows a block diagram of an oximeter probe in an implementation.

FIG. 4 shows a flow diagram of a method for determining opticalproperties of tissue (e.g., real tissue) by the oximeter probe in animplementation.

FIG. 5 shows a flow diagram of a method for determining opticalproperties of tissue by the oximeter probe in an implementation.

FIG. 6 shows a flow diagram of a method for determining opticalproperties of tissue by the oximeter probe in an implementation.

FIG. 7 shows an example graph of a reflectance curve, which may be for aspecific configuration of source structures and detector structures,such as the configuration source structures and detector structures ofthe probe tip.

FIG. 8 shows a graph of the absorption coefficient μ_(a) in arbitraryunits versus wavelength of light for oxygenated hemoglobins,deoxygenated hemoglobins, melanin, and water in tissue.

FIG. 9 shows a table for a database for a homogeneous model of tissue ofsimulated reflectance curves that is stored in the memory of theoximeter probe in an implementation.

FIG. 10 shows a table for a database for a layered model of tissue ofsimulated reflectance curves that is stored in the memory of theoximeter probe in an implementation.

FIGS. 11A-11B show a table for a database for a layered model of tissuewhere each row in the database is for four simulated reflectance curvesfor the four wavelengths of light emitted from the simulated sourcestructures and detected by the simulated detector structures.

FIGS. 12A-12B show a flow diagram of a method for determining theoptical properties of tissue (e.g., real tissue) by the oximeter probewhere the oximeter probe uses reflectance data and the simulatedreflectance curves to determine the optical properties.

FIG. 13 shows a flow diagram of another method for determining theoptical properties of tissue by the oximeter probe.

FIG. 14 shows a flow diagram of a method for weighting reflectance datagenerated by select detector structures.

FIG. 15 shows a flow diagram of a method for determining relative tissueparameters for tissue measured by the oximeter probe where contributionsfrom melanin in the tissue are removed from the relative tissueparameters.

FIGS. 16A and 16B show example graphs of absorption coefficients for thefirst target tissue and the second target tissue illuminated by a numberof light wavelengths, such as the 760 nanometers, 810 nanometers, 845nanometers, and 895 nanometers. Other wavelengths can be used by theoximeter probe including more or fewer wavelengths of light.

FIG. 17A shows an example curve of the absorption coefficients for thesecond target tissue (e.g., breast being reconstructed). The examplecurve has a negative slope along the entire length of the curve.

FIG. 17B shows an example curve of the first derivative of theabsorption coefficients with respect to wavelength for the first targetsite.

FIG. 17C shows an example curve of the second derivative of theabsorption coefficients with respect to wavelength for the first targetsite.

FIG. 17D shows an example first curve (e.g., first spectrum) and anexample second curve (e.g., second spectrum) of the absorptioncoefficients for the first target tissue (e.g., healthy breast tissue)and the second target tissue (e.g., reconstructed breast tissue).

FIG. 17E shows a first example plot (e.g., three top points) of thefirst derivative of the absorption coefficients with respect towavelength for the first target tissue and shows a second plot (e.g.,three bottom points) of the first derivative of the absorptioncoefficients with respect to wavelength for the second target tissue.

FIG. 17F shows a first example plot (e.g., two bottom points) of thesecond derivative of the absorption coefficients with respect towavelength for the first target site and shows a second example plot(e.g., two top points) of the second derivative of the absorptioncoefficients with respect to wavelength for the second target site.

FIG. 18 shows a vector in “angle” space for the values of the secondderivatives and plotted against each other.

FIG. 19 shows the first vector (θ₁, Φ₁) and a second vector 1903 (θ₂,Φ₂) in “angle” space.

FIG. 20 shows one of the delta angles Δθ and ΔΦ in vector space.

FIG. 21A shows a graph for the absorption coefficients (e.g., spectrum)for the fully oxygenated measurements and a graph 21 for the absorptioncoefficients for the fully deoxygenated measurements.

FIG. 21B shows a graph for the first derivative of the fully oxygenatedspectrum with respect to wavelength and a graph for the first derivativewith respect to wavelength of the fully deoxygenated spectrum.

FIG. 21C shows a graph for the second derivative with respect towavelength of the fully oxygenated spectrum and a graph for the secondderivative with respect to wavelength of the fully deoxygenatedspectrum.

FIG. 22 shows the vector (Δθ, ΔΦ) in angle space where Δθ and ΔΦ areplotted against each other.

FIG. 23 shows the baseline corrected vector and the calculated vectorcorrected for the phantom corrected by the scaling factor for thedifference in blood volume between the blood volume for the phantom andpatient tissue.

FIG. 24 shows the shows the vector for patient tissue projected onto thevector for the phantom.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 shows an image of an oximeter probe 101 in an implementation.Oximeter probe 101 is configured to make tissue oximetry measurements,such as intraoperatively and postoperatively. Oximeter probe 101 may bea handheld device that includes a probe unit 105, probe tip 110 (alsoreferred to as a sensor head), which may be positioned at an end of asensing arm 111. Oximeter probe 101 is configured to measure the oxygensaturation of tissue by emitting light, such as near-infrared light,from probe tip 110 into tissue, and collecting light reflected from thetissue at the probe tip.

Oximeter probe 101 includes a display 115 or other notification devicethat notifies a user of oxygen saturation measurements made by theoximeter probe. While probe tip 110 is described as being configured foruse with oximeter probe 101, which is a handheld device, probe tip 110may be used with other oximeter probes, such as a modular oximeter probewhere the probe tip is at the end of a cable device that couples to abase unit. The cable device might be a disposable device that isconfigured for use with one patient and the base unit might be a devicethat is configured for repeated use. Such modular oximeter probes arewell understood by those of skill in the art and are not describedfurther.

FIG. 2 shows an end view of probe tip 110 in an implementation. Probetip 110 is configured to contact tissue (e.g., a patient's skin) forwhich a tissue oximetry measurement is to be made. Probe tip 110includes first and second source structures 120 a and 120 b (generallysource structures 120) and includes first, second, third, fourth, fifth,sixth, seventh, and eighth detector structures 125 a-125 h (generallydetector structures 125). In alternative implementations, the oximeterprobe includes more or fewer source structures, includes more or fewerdetector structures, or both.

Each source structure 120 is adapted to emit light (such as infraredlight) and includes one or more light sources, such as four lightsources that generate the emitted light. Each light source can emit oneor more wavelengths of light. Each light source can include a lightemitting diode (LED), a laser diode, an organic light emitting diode(OLED), a quantum dot LED (QMLED), or other types of light sources.

Each source structure can include one or more optical fibers thatoptically link the light sources to a face 127 of the probe tip. In animplementation, each source structure includes four LEDs and includes asingle optical fiber that optically couples the four LEDs to the face ofthe probe tip. In alternative implementations, each source structureincludes more than one optical fiber (e.g., four optical fibers) thatoptically couples the LEDs to the face of the probe tip.

Each detector structure includes one or more detectors. In animplementation, each detector structure includes a single detectoradapted to detect light emitted from the source structures and reflectedfrom tissue. The detectors can be photodetectors, photoresistors, orother types of detectors. The detector structures are positioned withrespect to the source structures such that two or more (e.g., eight)unique source-to-detector distances are created.

In an implementation, the shortest source-to-detector distances areapproximately equal. For example, the shortest source-to-detectordistances are approximately equal between source structure 120 a anddetector structure 125 d (S1-D4) and between source structure 120 b anddetector structure 125 a (S2-D8) are approximately equal. The nextlonger source-to-detector distances (e.g., longer than each of S1-D4 andS2-D8) between source structure 120 a and detector structure 125 e(S1-D5) and between source structure 120 b and detector structure 125 a(S2-D1) are approximately equal. The next longer source-to-detectordistances (e.g., longer than each of S1-D5 and S2-D1) between sourcestructure 120 a and detector structure 125 c (S1-D3) and between sourcestructure 120 b and detector structure 125 g (S2-D7) are approximatelyequal. The next longer source-to-detector distances (e.g., longer thaneach of S1-D3 and S2-D7) between source structure 120 a and detectorstructure 125 f (S1-D6) and between source structure 120 b and detectorstructure 125 b (S2-D2) are approximately equal. The next longersource-to-detector distances (e.g., longer than each of S1-D6 and S2-D2)between source structure 120 a and detector structure 125 c (S1-D2) andbetween source structure 120 b and detector structure 125 f (S2-D6) areapproximately equal. The next longer source-to-detector distances (e.g.,longer than each of S1-D2 and S2-D6) between source structure 120 a anddetector structure 125 g (S1-D7) and between source structure 120 b anddetector structure 125 c (S2-D3) are approximately equal. The nextlonger source-to-detector distances (e.g., longer than each of S1-D7 andS2-D3) between source structure 120 a and detector structure 125 a(S1-D1) and between source structure 120 b and detector structure 125 e(S2-D5) are approximately equal. The next longer source-to-detectordistances (e.g., longest source-to-detector distance, longer than eachof S1-D1 and S2-D5) between source structure 120 a and detectorstructure 125 h (S1-D8) and between source structure 120 b and detectorstructure 125 d (S2-D4) are approximately equal. In otherimplementations, the source-to-detector distance can all be unique orhave fewer then eight distances that are approximately equal.

Table 1 below shows the eight unique source-to-detector distancesaccording to an implementation. The increase between nearestsource-to-detector distances is approximately 0.4 millimeters.

TABLE 1 Source-to- Source-to-Detector Detector Pairs DistancesMillimeters (S1-D4) 1.005 (S2-D8) 1.005 (S1-D5) 1.446 (S2-D1) 1.446(S1-D3) 1.883 (S2-D7) 1.883 (S1-D6) 2.317 (S2-D2) 2.317 (S1-S2) 2.749(S1-S2) 2.749 (S1-D7) 3.181 (S2-D3) 3.181 (S1-D1) 3.613 (S2-D5) 3.613(S1-D8) 4.004 (S2-D4) 4.004

In an implementation, detector structures 125 a and 125 e aresymmetrically positioned about a point that is on a straight lineconnecting sources 120 a and 120 b. Detector structures 125 b and 125 fare symmetrically positioned about the point. Detector structures 125 cand 125 g are symmetrically positioned about the point. Detectorstructures 125 d and 125 h are symmetrically positioned about the point.The point can be centered between source structures 120 a and 120 b onthe connecting line.

A plot of source-to-detector distance verses reflectance detected bydetector structures 125 can provide a reflectance curve where the datapoints are well spaced along the x-axis. These spacings of the distancesbetween source structures 120 a and 120 b, and detector structures 125reduces data redundancy and can lead to the generation of relativelyaccurate reflectance curves.

In an implementation, the source structures and detector structures canbe arranged at various positions on the probe surface to give thedistances desired (such as indicated above). For example, the twosources form a line, and there will be equal number of detectors aboveand below this line. And the position of a detector (above the line)will have point symmetry with another detector (below the line) about aselected point on the line of the two sources. As an example, theselected point may be the middle between the two sources, but notnecessarily. In other implements, the positioning can be arranged basedon a shape, such as a circle, an ellipse, an ovoid, randomly,triangular, rectangular, square, or other shape.

The following patent applications describe various oximeter devices andoximetry operation, and discussion in the following applications can becombined with aspects of the invention described in this application, inany combination. The following patent application are incorporated byreference along with all references cited in these application Ser. No.14/944,139, filed Nov. 17, 2015, Ser. No. 13/887,130 filed May 3, 2013,Ser. No. 15/163,565, filed May 24, 2016, Ser. No. 13/887,220, filed May3, 2013, Ser. No. 15/214,355, filed Jul. 19, 2016, Ser. No. 13/887,213,filed May 3, 2013, Ser. No. 14/977,578, filed Dec. 21, 2015, Ser. No.13/887,178, filed Jun. 7, 2013, Ser. No. 15/220,354, filed Jul. 26,2016, Ser. No. 13/965,156, filed Aug. 12, 2013, Ser. No. 15/359,570,filed Nov. 22, 2016, Ser. No. 13/887,152, filed May 3, 2013, Ser. No.29/561,749, filed Apr. 16, 2016, 61/642,389, 61/642,393, 61/642,395,61/642,399 filed May 3, 2012, and 61/682,146, filed Aug. 10, 2012.

FIG. 3 shows a block diagram of oximeter probe 101 in an implementation.Oximeter probe 101 includes display 115, a processor 116, a memory 117,a speaker 118, one or more user-selection devices 119 (e.g., one or morebuttons, switches, touch input device associated with display 115), aset of source structures 120, a set of detector structures 125, and apower source (e.g., a battery) 127. The foregoing listed components maybe linked together via a bus 128, which may be the system busarchitecture of oximeter probe 101. Although this figure shows one busthat connects to each component, the busing is illustrative of anyinterconnection scheme serving to link these components or othercomponents included in oximeter probe 101. For example, speaker 118could be connected to a subsystem through a port or have an internaldirect connection to processor 116. Further, the components describedare housed in a mobile housing (see FIG. 1 ) of oximeter probe 101 in animplementation.

Processor 116 may include a microprocessor, a microcontroller, amulti-core processor, or other processor type. Memory 117 may include avariety of memories, such as a volatile memory 117 a (e.g., a RAM), anonvolatile memory 117 b (e.g., a disk or FLASH). Differentimplementations of oximeter probe 101 may include any number of thelisted components, in any combination or configuration, and may alsoinclude other components not shown.

Power source 127 can be a battery, such as a disposable battery.Disposable batteries are discarded after their stored charge isexpended. Some disposable battery chemistry technologies includealkaline, zinc carbon, or silver oxide. The battery has sufficientstored charged to allow use of the handheld device for several hours. Inan implementation, the oximeter probe is a disposable.

In other implementations, the battery is rechargeable where the batterycan be recharged multiple times after the stored charge is expended.Some rechargeable battery chemistry technologies include nickel cadmium(NiCd), nickel metal hydride (NiMH), lithium ion (Li-ion), and zinc air.The battery can be recharged, for example, via an AC adapter with cordthat connects to the handheld unit. The circuitry in the handheld unitcan include a recharger circuit (not shown). Batteries with rechargeablebattery chemistry may be sometimes used as disposable batteries, wherethe batteries are not recharged but disposed of after use.

Tissue Analysis. FIG. 4 shows a flow diagram of a method for determiningoptical properties of tissue (e.g., real tissue) by oximeter probe 101in an implementation. Oximeters are used to measure tissue with melanin,such as skin. Melanin affects oxygen saturation measurements becausemelanin absorbs light. Skin colors vary widely from person to person,from very dark skin to very light skin. Depending on the amount ofmelanin present, which will vary depending, for example, on the skincolor, the amount of absorption can have a substantial effect on themeasurement, making the measured value inaccurate.

Therefore, there is a need for an oximeter that takes into account amelanin component of the tissue being measured, so that the measuredoxygen saturation value is accurate regardless of the skin color. Themeasured oxygen saturation value which accounts and compensates for themelanin component of the tissue can be referred to as amelanin-corrected oxygen saturation value.

The melanin in skin is eumelanin and pheomelanin (e.g., two melanincomponents), which are naturally occurring melanins in various relativepercentages. In contrast, most internal organs and tissue in the humanbody do not have melanin. Thus, there is no need to account for melaninwhen using an oximeter to make oxygen saturation measurements for suchinternal tissue. However, the human brain has neuromelanin, which is notpresent elsewhere in the body, especially the skin.

In an implementation, the oximeter determines and corrects for melaninabsorption in skin tissue (and any other tissue) that have eumelanin andpheomelanin pigments. This oximeter does not account and correct forneuromelanin or synthetic melanins since these are not found in theskin. In other implementations, the oximeter determines and corrects formelanin absorption due to a variety of melanins, including eumelanin,pheomelanin, or neuromelanin or synthetic melanins, or any combinationof these.

The oximeter probe uses determined melanin content (e.g., eumelanincontent, pheomelanin content, or both eumelanin and pheomelanincontents) for the tissue to correct various tissue parameters that aremeasured by the oximeter probe. The flow diagram represents one exampleimplementation. Steps may be added to, removed from, or combined in theflow diagram without deviating from the scope of the implementation.

At 400, a melanin reader optically couples (e.g., contacts) to thetissue. Melanin readers are optoelectronic devices that are adapted foremitting light, step 405, into tissue, and detecting the light, step410, after having been transmitted through the tissue or reflected fromthe tissue. The light detected by the melanin reader is converted toelectrical signals, step 415, that are used by the device to determinemelanin content of the tissue, step 420. The melanin reader can output avalue for the melanin content, step 425, on a display of the reader orvia a wired or wireless output. The value for melanin content representsvalues for eumelanin content, pheomelanin content, or both eumelanin andpheomelanin contents

In an implementation, at 430, information (e.g., a numerical value)about the melanin content is entered into oximeter probe 101. Theinformation can be entered into the oximeter probe via a user (e.g., ahuman user) or via a wired or wireless communication between the melaninreader and the oximeter probe.

In a first implementation, at 435, the oximeter probe uses theinformation for the melanin content to adjust one or more measuredvalues generated by the probe. In an implementation, the oximeter probedetermines a value for the oxygen saturation of the tissue. The oximeterprobe thereafter adjusts the value for the oxygen saturation using theinformation for the melanin content (e.g., information for eumelanincontent, pheomelanin content, or both eumelanin and pheomelanincontents). The oximeter probe can adjust the value for the oxygensaturation via one or more arithmetic operations, mathematicalfunctions, or both. For example, the information for the melanin contentcan be used as an offset (e.g., additive offset), a scale factor, orboth for adjusting the value for the oxygen saturation.

In an alternative implementation, at 435, the oximeter probe determinesthe absorption coefficient μ_(a) (mua), the reduced scatteringcoefficient μ_(s)′ (mus prime), or both for the tissue for a number ofwavelengths of light (e.g., four wavelengths of light) emitted anddetected by the oximeter probe. Thereafter, the oximeter probe adjuststhe determined absorption (μ_(a)) values for each wavelength of lightusing the information about melanin content (e.g., eumelanin content,pheomelanin content, or both eumelanin and pheomelanin contents). Theoximeter probe can adjust the absorption (μ_(a)) values via one or morearithmetic operations, mathematical functions, or both. For example, theinformation for the melanin content can be used as an offset (e.g.,additive offset), a scale factor, or both for adjusting the absorption(μ_(a)) values. Thereafter, the oximeter probe uses the absorption(μ_(a)) values to determine a value for the oxygen saturation for thetissue. Determination of absorption (μ_(a)) and reduced scattering GOare described below.

In another implementation, at 435, the oximeter probe applies one ormore melanin correction functions to reflectance data generated by thedetector structures. The melanin correction functions are based on theinformation for the melanin content (e.g., eumelanin content,pheomelanin content, or both eumelanin and pheomelanin contents). Thereflectance data can be analog reflectance data generated by thedetector structures prior to being digitized by one or more electroniccomponents of the oximeter probe or the reflectance data can bedigitized reflectance data. The melanin correction functions can beapplied to the analog reflectance data or the digitized reflectancedata. The melanin correction function includes one or more mathematicaloperations that are applied to the reflectance data. The scale factorsare determined by the oximeter probe based on information for themelanin content that is entered into the oximeter probe. The reflectancedata can be adjusted for melanin content for each wavelength of lightemitted by the oximeter probe.

In an implementation, the melanin correction function can be a combinedfunction (e.g., having scale factors) that is combined with one or morecalibration functions (e.g., having scale factors). The calibrationfunction can include scale factors for correcting the detector responsesbased on a variety of factors, such as differences that occur as aresult of manufacturing, that occur as a result of temperature drift ofthe detector structures, or other considerations. After the reflectancedata are adjusted by the oximeter probe, the probe can then determinethe oxygen saturation of blood in the tissue to be measured.

FIG. 5 shows a flow diagram of a method for determining opticalproperties of tissue by oximeter probe 101 in an implementation. Theoximeter probe uses information about the melanin content (e.g.,information for eumelanin content, pheomelanin content, or botheumelanin and pheomelanin contents) for the tissue to correct varioustissue parameters measured by the oximeter probe. The flow diagramrepresents one example implementation. Steps may be added to, removedfrom, or combined in the flow diagram without deviating from the scopeof the implementation.

At 500, the color of the tissue is compared to two or more color samplesof a number of color samples (sometimes referred to as color swatches)to determine whether the color of one of the color samples approximatelymatches the color of the tissue. Each color sample used for the colorcomparison is associated with a value of melanin content. Information(e.g., a numerical value) that identifies the melanin content for thecolor sample can be located on the color sample. In an implementation,the colors are the Pantone® colors of Pantone LLC of Carlstadt, N.J.

The comparison between the color of the tissue and the color of thecolor samples can be performed by a color comparison tool, such as oneor more of the color comparison tools of X-Rite, Incorporated of GrandRapids Mich. In an implementation, the comparison can be performedvisually by a human, such as the patient or a medical provider. In animplementation, the oximeter probe is adapted to determine a value forthe melanin content of the tissue, which can displayed on the display ofthe probe.

At 505, subsequent to the comparison, the value for the melanin contentof the tissue is determined based on the comparison.

In an alternative implementation, the value for the melanin content isdetermined from an estimate of the content based on a finite range ofmelanin content values. The number of values in a range for melanincontent can include two or more values.

For example, the number of values in a range for melanin contents can be2 (e.g., binary levels), such as 1 for light colored tissue (e.g., firstskin level for first level of melanin content) and 2 for dark tissue(e.g., second skin level for second level of melanin content), can be 3(e.g., 1 for light tissue color, 2 for medium color tissue, darker than1, and 3 for dark color tissue, darker than 1 and 2), or 4, 5, 6, 7, 8,9, 10 or more values for different skin colors. An estimation of thevalue for melanin content can be provided by the patient or a medicalprovider.

At 510, the information about the melanin content can be entered intothe oximeter probe. Step 510 can be skipped in a method where theoximeter probe determines the value for the melanin content. Button 119can be activated a predetermined number of times to place the oximeterprobe into a data entry mode in which the information for the melanincontent can be entered. The information for the melanin content canthereafter be entered into the probe by further activation of thebutton, via a wired communication with the probe, via a wirelesscommunication with the probe, via the display if the display is a touchinterface display, via an audible interface (e.g., a microphone andvoice recognition software in the probe), or by other input techniques.Alternatively, the button interface can provide (e.g., via interactionwith the processor) on screen selectable options (or options otherwiseprovided, such as lighted LEDs) for melanin content (e.g., 1 and 2 forlight and dark skin, 1, 2, and 3 for light, medium, and dark coloredskin, or more user selectable levels). Where the display is a touchinterface display, the user selectable options for melanin level can bedisplayed on the display where a user can touch there selection. Inimplementation of the oximeter device the operate with other user inputdevice (e.g., mouse, external keyboard, or others) the user can selectthe user selectable melanin options using one or more of these devices.

At 515, the oximeter probe is adapted to use information about themelanin content to adjust one or more measurements or calculationsperformed by the oximeter probe. For example, the oximeter probe can usethe information to adjust oxygen saturation value for the tissue, adjustabsorption (μ_(a)), adjust reduced scattering (μ_(s)′), adjust valuesgenerated by the detector or detectors, or one or more of a combinationof these adjustments. Each of these adjustments is described furtherabove with respect to step 435.

FIG. 6 shows a flow diagram of a method for determining opticalproperties of tissue by oximeter probe 101 in an implementation. Theoximeter probe uses the determined melanin content of the tissue tocorrect various tissue parameters that are measured by the probe. Theflow diagram represents one example implementation. Steps may be addedto, removed from, or combined in the flow diagram without deviating fromthe scope of the implementation.

At 600, one or more contralateral measurements of the tissue are madewith the oximeter probe. The contralateral measurements are made usingthe oximeter probe on a portion of tissue (e.g., healthy breast tissue,left breast, left side of a breast) before a measurement is made usingthe oximeter probe on target tissue that is to be measured (e.g., breasttissue for which tissue health is to be determined, e.g., right breast,right side of a breast where the first target tissue is the left side ofthe breast). The contralateral measurements of the tissue can be madefor each wavelength of light emitted by the oximeter probe.

At 605, reflectance data generated by the detector structures aredigitized by the electronic elements of the oximeter probe and arestored in memory. The reflectance data provide a basis of comparison forsubsequent tissue measurement. For example, the contralateralmeasurements provide baseline measurements of the melanin content of thecontralateral tissue where the baseline measurements can be used by theprocessor to correct for various measurements made the oximeter probe.

At 610, oximetry measurements of the target tissue to be measured aremade by the oximeter probe.

At 615, in an implementation, the processor generates oxygen saturationvalues for target tissue using the oximetry measurements. Thereafter,the processor retrieves the stored reflectance data stored at 605 forthe contralateral tissue and uses the retrieved values to adjust theoxygen saturation values. That is, the processor uses the baselinemeasurement for melanin content for the healthy contralateral tissuestissue to adjust the oxygen saturation values of the target tissue.

At 615, in an alternative implementation, the processor determinesabsorption μ_(a), reduced scattering coefficient μ_(s)′, or both fromthe oximetry measurements of the target tissue. Thereafter, theprocessor retrieves the reflectance data stored at 605 for thecontralateral tissue and uses the retrieved values to adjust μ_(a),μ_(s), or both. The processor then uses the adjusted μ_(a) value tocalculate values for oxygenated hemoglobin, deoxygenated hemoglobin, orother values for the target tissue. That is, the processor uses thebaseline measurement for melanin content of the healthy contralateraltissue to adjust μ_(a) for the target tissue.

At 615, in an another alternative implementation, the processorretrieves the stored reflectance data stored at 605 for thecontralateral tissue and uses the retrieved values to adjust thereflectance data generated by the detector structures for the targettissue. The adjustments applied by the processor to the reflectance datacan be simple offsets (e.g., addition offsets), scale factors (e.g.,multiplication offsets), functional corrections, other corrections, orany one or these adjustments in any combination. That is, the processoradjusts the values generated by the detector structures using thebaseline measurement for melanin content for the healthy tissue toadjust the reflectance data for the target tissue.

Stored Simulated Reflectance Curves. According to an implementation,memory 117 stores a number of Monte-Carlo-simulated reflectance curves315 (“simulated reflectance curves”), which may be generated by acomputer for subsequent storage in the memory. Each of the simulatedreflectance curves 315 represents a simulation of light (e.g., nearinfrared light) emitted from one or more simulated source structuresinto simulated tissue and reflected from the simulated tissue into oneor more simulated detector structures. Simulated reflectance curves 315are for a specific configuration of simulated source structures andsimulated detector structures, such as the configuration of sourcestructures 120 a-120 b and detector structures 125 a-125 h of probe tip110 having the source-to-detector spacing described above with respectto FIG. 2 .

Therefore, simulated reflectance curves 315 model light emitted from thesource structures and collected by the detector structures of oximeterprobe 101. Further, each of the simulated reflectance curves 315represents a unique real tissue condition, such as specific tissueabsorption and tissue scattering values that relate to particularconcentrations of tissue chromophores and particular concentrations oftissue scatterers. For example, the simulated reflectance curves can begenerated for simulated tissue having various melanin contents, variousoxygenated hemoglobin concentrations, various deoxygenated hemoglobinconcentrations, various concentrations of water, a static value for theconcentrations of water, various concentration of fat, a static valuefor the concentration of fat, or various absorption (μ_(a)) and reducedscattering (μ_(s)′) values.

The number of simulated reflectance curves stored in memory 117 may berelatively large and can represent nearly all, if not all, practicalcombinations of optical properties and tissue properties that may bepresent in real tissue that is analyzed for viability by oximeter probe101. While memory 117 is described as storing Monte-Carlo-simulatedreflectance curves, memory 117 may store simulated reflectance curvesgenerated by methods other than Monte-Carlo methods, such as using adiffusion approximation.

FIG. 7 shows an example graph of a reflectance curve, which may be for aspecific configuration of source structures 120 and detector structures125, such as the configuration source structures and detector structuresof probe tip 110. The horizontal axis of the graph represents thedistances between source structures 120 and detector structures 125(i.e., source-to-detector distances). If the distances between sourcestructures 120 and detector structures 125 are appropriately chosen, andthe simulated reflectance curve is a simulation for source structures120 and detector structures 125, then the lateral spacings between thedata points in the simulated reflectance curve will be relativelyuniform. Such uniform spacings can be seen in the simulated reflectancecurve in FIG. 7 . The vertical axis of the graph represents thesimulated reflectance of light that reflects from tissue and is detectedby detector structures 125. As shown by the simulated reflectance curve,the reflected light that reaches detector structures 125 varies with thedistance between source structures and detectors structures, with thereflected light detected at smaller source-to-detectors distancesgreater than the reflected light detected a larger source-to-detectordistances.

FIG. 8 shows a graph of the absorption coefficient μ_(a) versuswavelength of light for some significant tissue chromophores: bloodcontaining oxygenated hemoglobin, blood containing deoxygenatedhemoglobin, melanin, and water. In an implementation, the Monte-Carlosimulations used for generating the simulated reflectance curve arefunctions of one or more select chromophores that may be present intissue. The chromophores can include melanin, oxygenated hemoglobin,deoxygenated hemoglobin, water, lipid, cytochrome, or otherchromophores, in any combination. Oxygenated hemoglobins, deoxygenatedhemoglobins, and melanin are the most dominant chromophores in tissuefor much of the visible and near-infrared spectral range.

In an implementation, memory 117 stores a select number of data pointsfor each of the simulated reflectance curves 315 and might not store theentirety of the simulated reflectance curves. The number of data pointsstored for each of the simulated reflectance curves 315 may match thenumber of source-detector pairs. For example, if probe tip 110 includestwo source structures 120 a-120 b and includes eight detector structures125 a-125 h, then oximeter probe 101 includes sixteen source-detectorpairs, and memory 117 may thus store sixteen select data points for eachof the simulated reflectance curves for each wavelength of light emittedby source structure 120 a or source structure 120 b. In animplementation, the stored data points are for the specificsource-to-detectors distances of probe tip 110, such as those shown inTable 1.

Thus, the simulated reflectance curve database stored in memory 117might be sized 16×5850 where sixteen points are stored per curve thatmay be generated and emitted by each source structure 120 and measuredby each detector structure 125, where there are a total of 5850 curvesspanning the optical property ranges. Alternatively, the simulatedreflectance curve database stored in memory 117 might be sized 16×4×5850where sixteen points are stored per curve for four different wavelengthsthat may be generated and emitted by each source structure and wherethere are a total of 5850 curves spanning the optical property ranges.The 5850 curves originate, for example, from a matrix of 39 scatteringcoefficients μ_(s)′ values and 150 absorption coefficient μ_(a) values.In other implementations, more or fewer simulated reflectance curves arestored in the memory. For example, the number of simulated reflectancecurves stored in memory can range from about 5000 curves, to about250,000 curves, to about 400,000 curves, or more.

The reduced scattering coefficient μ_(s)′ values might range from 5:5:24per centimeter. The μ_(a) values might range from 0.01:0.01:1.5 percentimeter. It will be understood that the foregoing described rangesare example ranges and the number source-detectors pairs, the number ofwavelengths generated and emitted by each source structure, and thenumber of simulated reflectance curves may be smaller or larger.

FIG. 9 shows a database 900 of simulated reflectance curves 315 that isstored in the memory of the oximeter probe in an implementation. Thedatabase is for a homogeneous model of tissue. Each row in the databaserepresents one simulated reflectance curve generated from a Monte-Carlosimulation for simulated light emitted into simulated tissue from twosimulated source structures (e.g., source structures 120 a-120 b) anddetected by eight simulated detector structures (e.g., detectorstructures 125 a-125 h) subsequent to reflection from the simulatedtissue. The Monte-Carlo simulations used for generating the simulatedreflectance curves for the databases are for a homogeneous tissue model.The simulated tissue for the homogeneous tissue model has homogeneousoptical properties from the tissue surface through the epidermis, thedermis, and the subcutaneous tissue. That is, the optical properties ofthe epidermis, dermis, and subcutaneous are the same for the Monte-Carlosimulations. In the database, each of the simulated reflectance curvesis associated with a value for absorption (μ_(a)) and a value forreduced scattering (μ_(s)′). Each of the simulated reflectance curves inthe database can be associated with values for other chromophores.

The database of simulated reflectance curves can include actual values(e.g., floating point values) for simulated reflectances or can includeindexed values (e.g., binary values) for the actual values for thesimulated reflectances. As shown in FIG. 9 , the database includesindexed values (e.g., binary values) for the actual values for thesimulated reflectances. The database can include binary words of avariety of lengths dependent, for example, on the accuracy of theentries. The binary words can be 2 bits long, 4 bits long, 8 bits long,16 bits long, 32 bits long, or other lengths.

In an implementation, one or more mathematical transforms are applied tothe simulated reflectance curves prior to entry of the values for thecurves into the database. The mathematical transforms can improve thefit of the reflectance data generated by the detector structures to thesimulated reflectance curves. For example, a log function can be appliedto the simulated reflectance curves to improve the fit of the measureddata generated by the detector structures to the simulated reflectancecurves.

When an oximetry measurement is made, the reflectance data for eachwavelength of emitted light is detected by the detector structures andfitted to the simulated reflectance curves of database 900 individually.For the reflectance data for each wavelength of emitted light fitted tothe simulated reflectance curves, the oximeter probe determinesabsorption μ_(a), reduced scattering μ_(s)′ or both of these values. Forexample, a first set of reflectance data for a first wavelength of lightis fitted to the simulated reflectance curves to determine one or moreof absorption μ_(a), and reduced scattering μ_(s)′ (e.g., a first set oftissue parameters). Fitting the reflectance data to the simulatedreflectance curves is described further below.

Thereafter, a second set of reflectance data for a second wavelength oflight is fitted to the simulated reflectance curves in database 900 todetermine one or more of absorption μ_(a), and reduced scattering μ_(s)′(e.g., a second set of tissue parameters) for the second wavelength.Thereafter, a third set of reflectance data for a third wavelength oflight is fitted to the simulated reflectance curves in database 900 todetermine one or more of absorption μ_(a), and reduced scattering μ_(s)′(e.g., a third set of tissue parameters). Thereafter, a fourth set ofreflectance data for a fourth wavelength of light is fitted to thesimulated reflectance curves in database 900 to determine one or more ofabsorption μ_(a), and reduced scattering μ_(s)′ (e.g., a fourth set oftissue parameters) for the fourth wavelength.

The four sets of tissue parameters can then be used by the oximeterprobe together to determine various values for the tissue, such asoxygenated hemoglobin concentration, deoxygenated hemoglobinconcentration, melanin content, or other parameters.

FIG. 10 shows a database 1000 of simulated reflectance curves that isstored in the memory of the oximeter probe in an implementation. Thedatabase is for a layered model of tissue (e.g. layered skin). TheMonte-Carlo simulations that generated the simulated reflectance curvesuse the layered tissue model for the simulations. The layered tissue caninclude two or more layers. In an implementation, the layered tissueincludes two layers of tissue. The two layers of tissue have differentoptical properties, such as different absorption μ_(a), reducedscattering μ_(s)′, or both of these properties.

In one implementation, a first simulated tissue layer is for theepidermis and a second simulated tissue layer is for the dermis. Thethickness of the epidermis used in the Monte-Carlo simulations can rangefrom about 40 microns to about 140 microns. For example, the thicknessfor the epidermis can be 40 microns, 50 microns, 60 microns, 70 microns,80 microns, 90 microns, 100 microns, 110 microns, 120 microns, 130microns, 140 microns, or other thickness. The thickness of the dermisused in the Monte-Carlo simulations can range from less than 1millimeter to an effectively infinite thickness, such as 12 millimetersor greater.

One or more optical properties of the epidermis can be varied when thesimulated reflectance curves are generated for the dermis. For example,melanin content can be varied for the epidermis when the simulationreflectance curves are generated for the dermis. Alternatively, μ_(a)can be varied for the epidermis when the simulation reflectance curvesare generated for the dermis.

In an implementation, database 1000 includes the simulated reflectancecurves for the light that is reflected by the combination of theepidermis and the dermis.

The reflectance data for each wavelength of light emitted by the sourcestructures and detected by the detector structures for real tissuemeasured by the oximeter probe is fit to the simulated reflectancecurves one at a time by the processor. Based on the fit to one or morethe simulated reflectance curves in the database, the oximeter probedetermines one or both of the absorption μ_(a) and reduced scatteringμ_(s)′ for the real tissue for one or both layers. From the absorption(μ_(a)) values determined for one layer, the oximeter probe determinesthe oxygenated and deoxygenated hemoglobin concentrations for thetissue.

FIGS. 11A-11B show a database 1110 of simulated reflectance curvesstored in the memory of the oximeter probe in an implementation. Thedatabase is for a layered model of tissue. Each row in the databaseincludes simulated reflectance curves for each of four wavelengths oflight emitted from the simulated source structures and detected bysimulated detector structures. Each row of four simulated reflectancecurves includes 16 values for each simulated reflectance curve. Morespecifically, each row includes 16 values for the 16 source-to-detectordistances for source structures 120 a-120 b and detector structures 125a-125 h. In total, each row includes 64 values for the four simulatedreflectance curves for four wavelengths of light emitted from the twosimulated source structures and detected by the eight simulated detectorstructures.

The layered model of tissue for database 1110 can include more or fewersimulated reflectance curves per row if more or fewer wavelengths areemitted from the source structures. Database 1110 can include more orfewer then 16 values for each of simulated reflectance curves if, forexample, one or more than two source structure is included in the probetip, more or fewer detector structures are included in the probe tip, orboth.

Each of the four simulated reflectance curves for each row of database1110 is associated with four tissue parameters, including melanincontent, blood volume, scattering, and oxygen saturation (the fractionof oxygenated hemoglobin relative to total hemoglobin for tissue). Moreof fewer tissue parameters can be included in database 1110.

When a set of detector values that are generated by detector structures125 a-125 h for tissue to be measured by the oximeter probe are fit bythe processor to one or more of the rows, the oximeter probe therebydetermines, in any combination, one or more of the tissue parameterssuch as melanin content, blood volume, scattering, and oxygensaturation. In an implementation, the oximeter probe is adapted todetermine the oxygen saturation for the tissue and display a value forthe oxygen saturation on the display.

As described briefly above, database 1110 includes simulated reflectancecurves 315 for a layered tissue model. The layers of the simulatedtissue can include the epidermis, the dermis, subcutaneous tissue, orany combination of one or more of these layers. The layers can includegreater resolution of skin morphology such as the reticular dermis andsuperficial plexus. The Monte-Carlo simulations that generate thesimulated reflectance curve can simulate the tissue for variouschromophores included in the tissue layers. For example, the Monte-Carlosimulations can use a tissue model for the epidermis having variousmelanin contents, but might not use a tissue model for epidermis thatincludes blood. The Monte-Carlo simulations can use a tissue model forthe dermis layer having various blood volumes and various oxygensaturations. In an implementation, the Monte-Carlo simulations do notuse a tissue model for dermis that includes melanin. Similarly, theMonte-Carlo simulations can use a tissue model of adipose tissue havingvarious blood volumes and various oxygen saturations. In animplementation, the Monte-Carlo simulations do not use a tissue modelfor adipose tissue that has melanin. The tissue models for the tissuelayers can include concentrations for other tissue chromophores, such aswater and fat where the concentrations for these chromophores arerelatively typical physiological values.

In an implementation, the various chromophore concentrations that theMonte-Carlo simulations use for generating the simulated reflectancecurves span a relatively large and relatively accurate range of actualphysiological values present in real tissue. The number of valuesincluded in the ranges of actual physiological values can by varied tobalance various parameters of tissue oximeter measurements. For example,the number of values used for the range of concentrations of thechromophores in simulated tissue can be relatively high or low andaffect the accuracy of measurements made by the oximeter probe. In animplementation, 355 values are used in the Monte-Carlo simulations forthe range of melanin content for light absorption in simulated epidermaltissue. In an implementation, 86 values are used in the Monte-Carlosimulations for the range of melanin content for light absorption insimulated dermal tissue. For scattering in both simulated epidermaltissue and simulated dermal tissue, 65 values are used in theMonte-Carlo simulations. In other implementations, the number of thesevalues is different.

Tissue Analysis. FIGS. 12A-12B show a flow diagram of a method fordetermining the optical properties of tissue (e.g., skin) by oximeterprobe 101 where the oximeter probe uses reflectance data and simulatedreflectance curves 315 to determine the optical properties. The opticalproperties may include the absorption coefficient μ_(a) and the reducedscattering coefficient μ_(s)′ of the tissue. A further method forconversion of the absorption coefficient μ_(a) of the tissue to oxygensaturation values for tissue is described in further detail below. Theflow diagram represents one example implementation. Steps may be addedto, removed from, or combined in the flow diagram without deviating fromthe scope of the implementation.

At 1200, oximeter probe 101 emits light (e.g., near infrared light) fromone of the source structures 120, such as source structure 120 a intotissue. The oximeter probe is generally in contact with the tissue whenthe light is emitted from the source structure. After the emitted lightreflects from the tissue, detector structures 125 detect a portion thislight, step 1205, and generate reflectance data points for the tissue,step 1210. Steps 1200, 1205, and 1210 may be repeated for multiplewavelengths of light (e.g., red, near infrared light, or both) and forone or more other source structures, such as source structure 120 b. Thereflectance data points for a single wavelength might include sixteenreflectance data points if, for example, tissue oximeter probe 115 hassixteen source-to-detector distances. The reflectance data points aresometimes referred to as an N-vector of the reflectance data points.

At 1215, the reflectance data points (e.g., raw reflectance data points)are corrected for gain of the source-detector pairs. During calibrationof the source-detector pairs, gain corrections are generated for thesource-detector pairs and are stored in memory 117. Generation of thegain corrections is described in further detail below.

At 1220, processor 116 fits (e.g., via a sum of squares errorcalculation) the reflectance data points to the simulated reflectancecurves 315 to determine the particular reflectance data curve that bestfits (i.e., has the lowest fit error) the reflectance data points. Thedatabase stored in the memory and fit to the reflectance data can bedatabase 900, database 1000, or database 1100. In a specificimplementation, a relatively small set of simulated reflectance curvesthat are a “coarse” grid of the database of the simulated reflectancecurves is selected and utilized for fitting step 1220. For example, fordatabase 900 given 39 scattering coefficient μ_(s)′ values and 150absorption coefficient μ_(a) values, a coarse grid of simulatedreflectance curves might be determined by processor 116 by taking every5th scattering coefficient μ_(s)′ value and every 8th absorptioncoefficients μ_(a) for a total of 40 simulated reflectance curves in thecoarse grid. It will be understood that the foregoing specific valuesare for an example implementation and that coarse grids of other sizesmight be utilized by processor 116. The result of fitting thereflectance data points to the coarse grid is a coordinate in the coarsegrid (μ_(a), μ_(s)′)_(coarse) of the best fitting simulated reflectancecurve. For database 1000, the coarse grid will cover absorption in eachlayer and reduced scattering. Each of the following steps for the methodfor database 1000 will be adjusted for μ_(a) of each layer and μ_(s)′.For database 1100, the coarse grid will cover melanin content, oxygensaturation, blood volume, and scattering. Each of the following stepsfor the method for database 1100 will be adjusted for melanin content,oxygen saturation, blood volume, and scattering instead of μ_(a) andμ_(s)′.

At 1225, the particular simulated reflectance curve from the coarse gridhaving the lowest fit error is utilized by processor 116 to define a“fine” grid of simulated reflectance curves where the simulatedreflectance curves in the fine grid are around the simulated reflectancecurve from the coarse grid having the lowest fit error.

That is, the fine grid is a defined size, with the lowest errorsimulated reflectance curve from the coarse grid defining the center ofthe fine grid. The fine grid may have the same number of simulatedreflectance curves as the coarse grid or it may have more or fewersimulated reflectance curves. The fine grid has a fineness so as toprovide a sufficient number of points to determine a peak surface arrayof nearby absorption coefficient μ_(a) values and scattering coefficientμ_(s)′ values, step 1235, in the fine grid. Specifically, a thresholdmay be set by processor 116 utilizing the lowest error value from thecoarse grid plus a specified offset. The positions of the scatteringcoefficient μ_(s)′ and the absorption coefficient μ_(a) on the fine gridthat have errors below the threshold may all be identified for use indetermining the peak surface array for further determining thescattering coefficient μ_(s)′ and the absorption coefficient μ_(a) forthe reflectance data. Specifically, an error fit is made for the peak todetermine the absorption coefficient μ_(a) and the scatteringcoefficient μ_(s)′ values at the peak. A weighted average (e.g., acentroid calculation) of the absorption coefficient μ_(a) and thescattering coefficient μ_(s)′ values at the peak may be utilized by theoximeter probe for the determination of the absorption coefficient μ_(a)and the scattering coefficient μ_(s)′ values for the reflectance datapoints for the tissue, step 1240.

Weights for the absorption coefficient μ_(a) and the scatteringcoefficient values for the weighted average may be determined byprocessor 116 as the threshold minus the fine grid error. Because pointson the fine grid are selected with errors below the threshold, thisgives positive weights. The weighted calculation of the weighted average(e.g., centroid calculation) renders the predicted scatteringcoefficient μ_(s)′ and absorption coefficient μ_(a) (i.e.,(μ_(a),μ_(s)′)_(fine)) for the reflectance data points for the tissue.Other methods may be utilized by the oximeter probe, such as fittingwith one or more of a variety of non-linear least squares to determinethe true minimum error peak for the absorption coefficient μ_(a).

In an implementation, processor 116 calculates the log of thereflectance data points and the simulated reflectance curves, anddivides each log by the square root of the source-to-detector distances(e.g., in centimeters). These log values divided by the square root ofthe of the source-to-detector distances may be utilized by processor 116for the reflectance data points and the simulated reflectance curves inthe foregoing described steps (e.g., steps 1215, 1220, 1225, and 1230)to improve the fit of the reflectance data points to the simulatedreflectance curves.

According to another implementation, the offset is set essentially tozero, which effectively gives an offset of the difference between thecoarse grid minimum and the fine grid minimum. The method describedabove with respect to FIGS. 12A-12B relies on minimum fit error from thecoarse grid, so the true minimum error on the fine grid is typicallylower. Ideally, the threshold is determined from the lowest error on thefine grid, which would typically require additional computation by theprocessor.

The following is a further detailed description for finding theparticular simulated reflectance curve that best fits the reflectancedata points in the fine grid in an implementation. FIG. 12B shows a flowdiagram of a method for finding the particular simulated reflectancecurve that best fits the reflectance data points in the fine grid in animplementation. The flow diagram represents one example implementation.Steps may be added to, removed from, or combined in the flow diagramwithout deviating from the scope of the implementation.

Subsequent to determining the particular simulated reflectance curve(μ_(a),μ_(s)′)_(coarse) from the coarse grid that best fits thereflectance data points at step 1225, processor 116 computes an errorsurface in a region about (μ_(a),μ_(s)′)_(coarse) in the full simulatedreflectance curve database (i.e., 16×5850 (μ_(a),μ_(s)′) database) ofsimulated reflectance curves, step 1250. The error surface is denotedas: err(μ_(a),μ_(s)′). Thereafter, processor 116 locates the minimumerror value in err(μ_(a),μ_(s)′), which is referred to as err_(min),step 1255. Processor 116 then generates a peak surface array fromerr(μ_(a),μ_(s)′) that is denoted bypksurf(μ_(a),μ_(s)′)=k+err_(min)−err(μ_(a),μ_(s)′) if the peak surfaceis greater than zero, orpksurf(μ_(a),μ_(s)′)=k+err_(min)−err(μ_(a),μ_(s)′)=0 if the peak surfaceis less than or equal to zero, step 1260. In the expression k is chosenfrom a peak at the minimum point of err(μ_(a), μ_(s)′) with a widthabove zero of approximately ten elements. The center-of-mass (i.e., thecentroid calculation) of the peak in pksurf(μ_(a),μ_(s)′) uses theheights of the points as weights, step 1265. The position of thecenter-of-mass is the interpolated result for the absorption coefficientμ_(a) and the scattering coefficient μ_(s)′ for the reflectance datapoints for the tissue

The method described above with respect to FIGS. 12A and 12B fordetermining the absorption coefficient μ_(a) and the scatteringcoefficient μ_(s)′ for reflectance data points for tissue may berepeated for each of the wavelengths (e.g., 3 or 4 wavelengths)generated by each of source structures 120.

Oxygen Saturation Determination. According to a first implementation,processor 116 determines the oxygen saturation for tissue that is probedby oximeter probe 101 by utilizing the absorption coefficients μ_(a)(e.g., 3 or 4 absorption coefficients μ_(a)) that are determined (asdescribed above) for the 3 or 4 wavelengths of light that are generatedby each source structure 120. According to a first implementation, alook-up table of oxygen saturation values is generated for finding thebest fit of the absorption coefficients μ_(a) to the oxygen saturation.The look-up table may be generated by assuming a range of likely totalhemoglobin, melanin, and oxygen saturation values and calculating μ_(a)for each of these scenarios. Then, the absorption coefficient μ_(a)points are converted to a unit vector by dividing by a norm of the unitvector to reduce systematic error and only depend on relative shape ofcurve. Then the unit vector is compared to the look-up table to find thebest fit, which gives the oxygen saturation.

According to a second implementation, processor 116 determines theoxygen saturation for the tissue by calculating the net analyte signal(NAS) of deoxygenated hemoglobin and oxygenated hemoglobin. The NAS isdefined as the portion of the spectrum that is orthogonal to the otherspectral components in the system. For example, the NAS of deoxygenatedhemoglobin in a system that also contains oxygenated hemoglobin anddeoxygenated hemoglobin is the portion of the spectrum that isorthogonal to the oxygenated hemoglobin spectrum and the melaninspectrum. The concentrations of deoxygenated and oxygenated hemoglobincan be calculated by vector multiplying the respective NAS by thepreviously determined absorption coefficients at each wavelength. Oxygensaturation is then readily calculated as the concentration of oxygenatedhemoglobin divided by the sum of oxygenated hemoglobin and deoxygenatedhemoglobin. Anal. Chem. 58:1167-1172 (1986) by Lorber is incorporated byreference and provides a framework for a further detailed understandingof the second implementation for determining the oxygen saturation forthe tissue.

In an implementation of oximeter probe 101, the reflectance data isgenerated by detector structures 125 at 30 Hertz, and oxygen saturationvalues are calculated at approximately 3 Hertz. A running average ofdetermined oxygen saturation values (e.g., at least three oxygensaturation values) may be displayed on display 115, which might have anupdate rate of 1 Hertz.

Optical Properties. As described briefly above, each simulatedreflectance curve 315 that is stored in memory 117 represents uniqueoptical properties of tissue. More specifically, the unique shapes ofthe simulated reflectance curves, for a given wavelength, representunique values of the optical properties of tissue, namely the scatteringcoefficient (μ_(s)), the absorption coefficient GO, the anisotropy ofthe tissue (g), and index of refraction of the tissue from which thetissue properties may be determined.

The reflectance detected by detector structures 125 for relatively smallsource-to-detector distances is primarily dependent on the reducedscattering coefficient, μ_(s)′. The reduced scattering coefficient is a“lumped” property that incorporates the scattering coefficient μ_(s) andthe anisotropy g of the tissue where μ_(s)′=μ_(s)(1−g), and is used todescribe the diffusion of photons in a random walk of many steps of sizeof 1/μ_(s)′ where each step involves isotropic scattering. Such adescription is equivalent to a description of photon movement using manysmall steps 1/μ_(s) which each involve only a partial deflection angleif there are many scattering events before an absorption event, i.e.,μ_(a)<<μ_(s)′.

In contrast, the reflectance that is detected by detector structures 125for relatively large source-to-detector distances is primarily dependenton the effective absorption coefficient μ_(eff), which is defined as√{square root over (3μ_(a)(μ_(a)+μ_(s)′))}, which is a function of bothμ_(a) and μ_(s)′.

Thus, by measuring reflectance at relatively small source-to-detectordistances (e.g., S1-D4 and S2-D8 of FIG. 2 ) and relatively largesource-to-detector distances (e.g., S1-D8 and S2-D4 of FIG. 2 ), bothμ_(a) and μ_(s)′ can be independently determined from one another. Theoptical properties of the tissue can in turn provide sufficientinformation for the calculation of oxygenated hemoglobin anddeoxygenated hemoglobin concentrations and hence the oxygen saturationof the tissue.

Iterative Fit for Data Collection Optimization. FIG. 13 shows a flowdiagram of another method for determining the optical properties oftissue by oximeter probe 101. The flow diagram represents one exampleimplementation. Steps may be added to, removed from, or combined in theflow diagram without deviating from the scope of the implementation.

At 1300, oximeter probe 101 emits light (e.g., near infrared light) fromone of the source structures, such as source structure 120 a intotissue. After the emitted light reflects from the tissue, detectorstructures 125 detect the light, step 1305, and generate reflectancedata for the tissue, step 1310. Steps 1300, 1305, and 1310 may berepeated for multiple wavelengths of light and for one or more othersource structures, such as source structure 120 b. At 1315, oximeterprobe 101 fits the reflectance data to simulated reflectance curves 315and determines the simulated reflectance curve to which the reflectancedata has the best fit. The database stored in the memory and fit to thereflectance data can be database 900, database 1000, or database 1100.Thereafter, oximeter probe 101 determines the optical properties (e.g.,μ_(a), and μ_(s)′ for database 900 or database 1000, or melanin content,oxygen saturation, blood volume, and scattering for database 1100) forthe tissue based on the optical properties of the simulated reflectancecurve that best fits the reflectance data, step 1320.

At 1325 oximeter probe 101 determines the mean free path of the light inthe tissue from the optical properties (e.g., mfp=1/(μ_(a)+μ_(s)′))determined at step 1320. Specifically, the mean free path can bedetermined from the optical properties obtained from a cumulativereflectance curve that includes the reflectance data for all of thesource-detector pairs (e.g., pair 1: source structure 120 a and detectorstructure 125 a; pair 2: source structure 120 a and detector structure125 b; pair 3: source structure 120 a and detector structure 125 c; pair4: source structure 120 a and detector structure 125 d; pair 5: sourcestructure 120 a and detector structure 125 e; pair 6: source structure120 a and detector structure 125 f; pair 7: source structure 120 a anddetector structure 125 g; pair 8: source structure 120 a and detectorstructure 125 h; pair 9: source structure 120 b and detector structure125 a, pair 10: source structure 120 b and detector structure 125 b . .. and others).

At 1330, oximeter probe 101 determines whether the mean free pathcalculated for a given region of the tissue is longer than two times theshortest source-to-detector distance (e.g., S1-D4 and S2-D8 of FIG. 2 ).If the mean free path is longer than two times the shortestsource-to-detector distance, then the collected reflectance data isre-fitted to the simulated reflectance curves (i.e., reanalyzed) withoututilizing the reflectance data collected from the detector structuresfor the source-to-detector pairs having the shortest source-to-detectordistance. For example, steps 1315-1330 are repeated without use of thereflectance data from detector structure 125 e with source structure 120a acting as the source for detector structure 125 d, and without use ofthe reflectance data from detector structure 125 h with source structure120 b acting as the source for detector structure 125 h. The process ofcalculating the mean free path and discarding the reflectance data forone or more source-detector pairs may be repeated until nosource-detector pairs that contribute reflectance data to the fit have asource-to-detector distance shorter than one half of the calculated meanfree path. Thereafter, oxygen saturation is determined from the bestfitting simulated reflectance curve and reported by oximeter probe 101,such as on display 115, step 1335.

Light that is emitted from one of the source structures 120 into tissueand that travels less than half of the mean free path is nondiffuselyreflected. The re-emission distance for this light is strongly dependenton the tissue phase function and the local tissue composition.Therefore, using the reflectance data for this light tends to result ina less accurate determination of the optical properties and tissueproperties as compared with the reflectance data for light that hasundergone multiple scattering events.

Data Weighting Detector Structures. Detector structures 125 that arepositioned at increasing distances from source structures 120 receivedecreasing amounts of reflectance from tissue. Therefore, thereflectance data generated by detector structures 125 having relativelyshort source-to-detector distances (e.g., S1-D4 and S2-D8 of FIG. 2 )tends to exhibit intrinsically higher signal compared to reflectancedata generated by detector structures having relatively longsource-to-detector distances (e.g., S1-D8 and S2-D4 of FIG. 2 ). Fitalgorithms may therefore preferentially fit the simulated reflectancecurves to the reflectance data that is generated by detector structures125 having relatively short source-to-detectors distances (e.g.,source-to-detector distances less than or equal to the average distancebetween the source structures and the detector structures) more tightlythan reflectance data that is generated by detector structures havingrelatively long source-to-detector distances (e.g., source-to-detectordistances greater than the average distance). For relatively accuratedetermination of the optical properties from the reflectance data, thisdistance-proportional skew may be undesirable and may be corrected byweighting the reflectance data as described immediately below.

FIG. 14 shows a flow diagram of a method for weighting reflectance datagenerated by select detector structures 125. The flow diagram representsone example implementation. Steps may be added to, removed from, orcombined in the flow diagram without deviating from the scope of theimplementation.

At 1400, oximeter probe 101 emits light from one of the sourcestructures, such as source structure 120 a into tissue. After theemitted light reflects from the tissue, detector structures 125 detectthe light, step 1405, and generate reflectance data for the tissue, step1410. Steps 1400, 1405, and 1410 may be repeated for multiplewavelengths of light and for one or more other source structures, suchas source structure 120 b. At 1415, oximeter probe 101 fits a firstportion of the reflectance data to the simulated reflectance curves 315.The database stored in the memory and fit to the reflectance data can bedatabase 900, database 1000, or database 1100. The first portion of thereflectance data is generated by a first portion of detector structuresthat are less than a threshold distance from the source structure. Thethreshold distance may be the average distances (e.g., approximatemidrange distance) between the source structures and the detectorstructures. At 1420, reflectance data for a second portion of thereflectance data is fitted to the simulated reflectance curves. Thesecond portion of reflectance data is generated by the first portion ofthe detector structures and another detector structure that is at thenext largest source-to-detector distance from the source compared to thethreshold distance. For example, if the first portion of detectorstructures includes detector structures 125 c, 125 d, 125 e, and 125 f,then the detector structure that is at the next largestsource-to-detector distance is detector structure 125 g (see table 1).

At 1425, the fit generated at step 1415 is compared to the fit generatedat step 1420 to determine whether the fit generated at step 1420 isbetter than the fit generated at 1415. As will be understood by those ofskill in the art, a “closeness” of a fit of data to a curve isquantifiable based on a variety of parameters, and the closeness of fitsare directly comparable to determine the data having a closer fit(closer fit) to a curve. As will be further understood, a closer fit issometimes also referred to as a better fit or a tighter fit. If the fitgenerated at step 1420 is better than the fit generated at step 1415,then steps 1420 and 1425 are repeated with reflectance data that isgenerated by detector structures that include an additional detectorstructure (according to the example being considered, detector structure125 c) that is positioned at a next increased source-to-detectordistance from the source. Alternatively, if the fit generated at step1420 is not better than the fit generated at step 1415, then thereflectance data for detector structures 125 that are positioned atsource-to-detector distances that are greater than the thresholddistance are not used in the fit. Thereafter, oximeter probe 101 usesthe fit generated at 1415 or step 1420 (if better than the fitdetermined at step 1415) to determine the optical properties and theoxygen saturation of the tissue, step 1430. Thereafter, oxygensaturation is reported by oximeter probe 101, such as on display 115,step 1435.

According to an alternative implementation, if the fit generated at step1420 is not better than the fit generated at step 1415, then thereflectance data are weighted by a weighting factor for detectorstructures that have source-to-detector distances that are greater thanthe threshold distance so that this weighted reflectance data has adecreased influence on the fit. Reflectance data that is not used in afit may be considered as having a zero weight and may be associated withreflectance from tissue below the tissue layer of interest. Reflectancefrom tissue below the tissue layer of interest is said to exhibit acharacteristic kink in the reflectance curve that indicates thisparticular reflectance.

It is noted that curve-fitting algorithms that fit the reflectance datato the simulated reflectance curves may take into account the amount ofuncertainty of the reflectance data as well as the absolute location ofthe reflectance data. Uncertainty in the reflectance data corresponds tothe amount of noise from the generation of the reflectance data by oneof the detector structures, and the amount of noise can scale as thesquare root of the magnitude of the reflectance data.

According to a further implementation, oximeter probe 101 iterativelyweights the reflectance data based on the amount of noise associatedwith the measurements of the reflectance data. Specifically, thereflectance data generated by detector structures having relativelylarge source-to-detector distances generally have lower signal-to-noiseratio compared to the reflectance data generated by detector structurehaving relatively short source-to-detector distances. Weighting thereflectance data generated by detector structures having relativelylarge source-to-detector distances allows for this data to contribute tothe fit equally or approximately equally to other reflectance data.

Methods described for matching reflectance data to a number ofMonte-Carlo-simulated reflectance curves provide for relatively fast andaccurate determination of the optical properties of real tissue probedby the oximeter probe. Speed in determining optical properties of tissueis an important consideration in the design of intraoperative probescompared to postoperative probes. Further, the Monte-Carlo methodsdescribed allow for robust calibration methods that in-turn allow forthe generation of absolute optical properties as compared with relativeoptical properties. Reporting absolute optical properties, as opposed torelative optical properties, is relatively important for intraoperativeoximeter probes as compared with post-operative oximeter probes.

FIG. 15 shows a flow diagram of a method for determining relative tissueparameters for tissue measured by the oximeter probe where contributionsfrom melanin in the tissue are removed from the relative tissueparameters. The flow diagram represents one example implementation.Steps may be added to, removed from, or combined in the flow diagramwithout deviating from the scope of the implementation.

The method includes making oximeter measurements on different tissuelocations (e.g., first and second target tissues) of a patient's body,and using the oximeter measurements to determine a relative tissueparameter for one of the target tissues (e.g., the second targettissue). The different target locations can be tissues that have thesame or similar melanin concentrations, such as contralateral tissues.For example, during a breast reconstruction surgery (e.g., where atissue flap is being used in the reconstruction), the first targettissue may be healthy breast tissue and the second target tissue may betissue for which an oximeter reading is desired (e.g., the breast thatis being reconstructed). The first breast tissue can be from the samebreast or different breast or other tissue, such as other chest tissue.The two tissues should have the same or similar melanin content. Theoximeter measurements for the first and second target tissue are thenused to generate a relative tissue parameter (e.g., relative oxygensaturation value) that is the difference between a first tissueparameter (e.g., first oxygen saturation) of the first target tissue(e.g., healthy breast tissue) and second tissue parameter (e.g., secondoxygen saturation) of the second target tissue (e.g., tissue flap beingused for the reconstruction or the breast tissue being reconstructed)where contributions from the light absorption by melanin is removed fromthe measure for the relative oxygen saturation.

As described further below, the method exploits the approximatelyconstant slope of the curve of the absorption coefficients of melanin intissue for light having wavelengths from about 700 nanometers to about900 nanometers (e.g., infrared wavelengths). The method uses aderivative approach of the absorption coefficients to remove the melanincontributions (e.g., from light absorption by melanin) from the oximetrymeasurements and determinations (e.g., final results, intermediaryresults, or both). See the slope for the absorption coefficients ofmelanin in FIG. 8 . The method also exploits the differences in theslope of the curves for the absorption coefficients of melanin andoxygenated blood hemoglobin and the differences in the slope of thecurves of the absorptions coefficients of melanin and deoxygenated bloodhemoglobin. See the curves for the absorption coefficients of oxygenatedand deoxygenated hemoglobin in FIG. 8 . Also, as further explainedbelow, the method exploits the changes in the slopes of curves for theabsorptions coefficients for the first and second target tissues wherethese tissues may have different concentrations of oxygenated anddeoxygenated hemoglobin.

In an implementation of the method, a user contacts the probe tip of theoximeter probe to the first target tissue at a first location (e.g.,different location from the second target tissue) in preparation to usethe probe for making an oximeter measurement. See 1500 in FIG. 15 .Thereafter, the oximeter probe emits light (e.g., 2, 3, 4, or morewavelengths of IR) from one or more of the source structures (e.g., twosource structures) on the probe face into the first target tissue. Thedetector structures on the probe face detect the light subsequent toreflection from or transmission through the first target tissue andgenerate first reflectance data based on the detected light. The firstreflectance data includes a first melanin absorption component ofreflectance data for melanin content (e.g., first melanin content forthe first target tissue) of the first target tissue. See 1505 in FIG. 15.

The oximeter probe then determines a number of first oximeter parametersfor the first target tissue using the first reflectance data for eachwavelength of light transmitted from the source structures into thetissue. See 1510 in FIG. 15 . The first oximeter parameters can bedetermined by the oximeter probe by fitting the reflectance data to thesimulated reflectance curves as described above. The oximeter probestores these first oximeter parameters in the memory of the probe. Thefirst oximeter parameters can be values for the absorption coefficientsfor each of the transmitted wavelengths of light for the first targettissue. The first oximeter parameters for the first target tissue (e.g.,healthy tissue) are baseline parameters. The first oximeter parameters(e.g., intermediary values, such as angular measure, absorptionscoefficients, oxygen saturation values, other values) may be unavailablefor display after the first measurement is made and before a secondmeasurement is made (e.g., described below at 1515, 1520, and 1525 ofFIG. 15 ).

FIGS. 16A and 16B show example graphs of absorption coefficients for thefirst target tissue and the second target tissue illuminated by a numberof light wavelengths, such as the 760 nanometers, 810 nanometers, 845nanometers, and 895 nanometers. Other wavelengths can be used by theoximeter probe including more or fewer wavelengths of light.

At 1515, the user moves the probe face of the oximeter probe to thesecond target tissue (e.g., breast tissue undergoing reconstructivesurgery). Thereafter, the oximeter probe emits light (e.g., 2, 3, 4, ormore wavelengths of IR) from the one or more source structures on theprobe face into the second target tissue. The detector structures on theprobe face detect the light subsequent to reflection from ortransmission through the second target tissue and generate secondreflectance data based on the detected light. The second reflectancedata includes a second melanin absorption component of reflectance datafor melanin content (e.g., second melanin content for the first targettissue) of the second target tissue. See 1520 in FIG. 15 .

The oximeter probe then determines a number of second oximeterparameters for the second target tissue using the second reflectancedata for each wavelength of light transmitted from the source structuresinto the tissue. See 1525 in FIG. 15 . The second oximeter parameterscan be determined by the oximeter probe by fitting the secondreflectance data to the simulated reflectance curves as described above.The oximeter probe can store these second oximeter parameters in thememory of the probe. The second oximeter parameters can be values forthe absorption coefficients for the transmitted wavelengths of light forthe second target tissue.

At 1530, the oximeter probe, determines a first angular deviation θ₁(see FIG. 16A) of the first curve (e.g., lines forming the curves) forfirst absorption coefficients for line 1605 (e.g., the projection 1605 aof line 1605 which is shown as a broken line in FIG. 16A) between 760nanometers and 810 nanometers and 1410 between 810 nanometers and 845nanometers.

The oximeter probe, determines a second angular deviation Φ₁ of thefirst curve (e.g., lines forming the curves) for the second absorptioncoefficients for line 1610 (e.g., the projection 1610 a of line 1610which is shown as a broken line in FIG. 16A) between 810 nanometers and845 nanometers and line 1615 between 845 nanometers and 890 nanometers.

The oximeter probe, determines a third angular deviation θ₂ (see FIG.16B) of the second curve (e.g., lines forming the curves) for the secondabsorption coefficients for line 1620 (e.g., the projection 1620 a ofline 1620 which is shown as a broken line in FIG. 16B) between 760nanometers and 810 nanometers and line 1625 between 810 nanometers and845 nanometers.

The oximeter probe, determines a fourth angular deviation Φ₂ of thesecond curve (e.g., lines forming the curves) for the second absorptioncoefficients for line 1625 (e.g., the projection 1625 a of line 1625which is shown as a broken line in FIG. 16B) between 810 nanometers and845 nanometers and line 1630 between 845 nanometers and 890 nanometers.

The first and second angular deviations θ₁ and Φ₁ shown in FIG. 16A arecalculated by the oximeter probe by taking the second derivative of thefirst curve for the absorption coefficients with respect to wavelengthfor the first target tissue (e.g., healthy breast tissue). The third andfourth angular deviations θ₂ and Φ₂ shown in FIG. 16B are calculated bythe oximeter probe by taking the second derivative of the first curvefor the absorption coefficients with respect to wavelength for thesecond target tissue (e.g., reconstructed breast tissue).

FIG. 17A shows an example curve (e.g., first spectrum) of the absorptioncoefficients for the first target tissue (e.g., healthy breast tissue).The example curve has a negative slope along the entire length of thecurve. FIG. 17B shows an example curve of the first derivative of theabsorption coefficients with respect to wavelength for the first targettissue. The plot in FIG. 17B is for wavelengths of between 750 and 850.The negative values of the example curve of FIG. 17B match the negativeslope shown in FIG. 17A, and the example curve has a positive slopealong the entire length of the curve. FIG. 17C shows an example curve ofthe second derivative of the absorption coefficients with respect towavelength for the first target site. The plot in FIG. 17C is forwavelengths of between 800 and 850 nanometers (e.g., specifically for810 nanometers and 845 nanometers). The positive values of the examplecurve shown in FIG. 17C match the positive slope of the curve in FIG.17B.

FIG. 17D shows an example first curve (e.g., first spectrum) 1701 and anexample second curve (e.g., second spectrum) 1703 of the absorptioncoefficients for the first target tissue (e.g., healthy breast tissue)and the second target tissue (e.g., reconstructed breast tissue). Therelatively small displacement of the curves indicates the relativelysmall change in the absorption coefficients between a first targettissue and a second target tissue. The example curves each has anegative slope along the entire length of the curve.

FIG. 17E shows a first example plot 1711 (e.g., three top points) of thefirst derivative of the absorption coefficients with respect towavelength for the first target tissue and shows a second plot 1713(e.g., three bottom points) of the first derivative of the absorptioncoefficients with respect to wavelength for the second target tissue.The plot in FIG. 17E is for wavelengths of between 750 and 850. Thenegative values of the example plots of FIG. 17E match the negativeslopes shown in FIG. 17D, and the example curves have positive slopesalong the entire lengths of the curves.

FIG. 17F shows a first example plot 1721 (e.g., to bottom points) of thesecond derivative of the absorption coefficients with respect towavelength for the first target site and shows a second example plot1723 (e.g., to top points) of the second derivative of the absorptioncoefficients with respect to wavelength for the second target site. Theplots in FIG. 17D are for wavelengths of between 800 and 850 nanometers(e.g., specifically for 810 nanometers and 845 nanometers). The positivevalues of the example plots shown in FIG. 17F match the positive slopeof the curve in FIG. 17E.

FIG. 18 shows a vector (θ₁, Φ₁) in “angle” space for the values of thesecond derivatives θ₁ and Φ₁ plotted against each other. In angle space,the vertical and horizontal axes are for values θ₁ and Φ₁ of the secondderivatives for two wavelengths of light. In the particular example, thevertical and horizontal axes are for values for the second derivativefor 810 nanometers and 845 nanometers. Other wavelength values from thesecond derivatives can be chosen is the tissue is illuminated by otherwavelengths of light. That is, the end point 1801 a of the vector 1801in angle space represents two values for the second derivative for thefirst tissue (e.g., healthy breast tissue) plotted against each other.

FIG. 19 shows the first vector 1901 (θ₁, Φ₁) and a second vector 1903(θ₂, Φ₂) in “angle” space. That is, θ₁, and Φ₁ are plotted against eachother and θ₂ and Φ₂ are plotted against each other. The differencebetween the two vector are the delta angles Δθ=θ₁−θ₂ and ΔΦ=Φ₁−Φ₂ andrepresents the changes in the curvature of the curves (also sometimesreferred to as spectra) for absorption coefficients for the first andsecond target tissues for wavelengths 810 and 845. The delta angles Δθand ΔΦ can be determined by the processor by projecting vector 1903 ontovector 1901. See FIG. 15 at 1535 and 1540.

FIG. 20 shows one of the delta angles Δθ and ΔΦ in vector space. Thechanges of curvature of the absorption coefficients are attributable torelative changes in the oxygen saturation between the first and secondtarget tissue sites. Because the curvatures for the absorptioncoefficients of melanin are fixed or approximately fixed for the firstand second target tissue (e.g., melanin concentrations are the same orsimilar for the first and second target tissue, single patient withcontralateral measurements), the changes of curvature of the absorptioncoefficients Δθ and ΔΦ are not attributable to melanin in the tissuesites. That is, any contribution to the second derivatives from melaningo to zero.

The relative change in oxygen saturation between the first and secondtarget tissues is calculated from the delta angles Δθ and ΔΦ and a value(e.g., percentage difference) for the relative change in oxygensaturation is displayed on the display of the oximeter probe. See FIG.15 at 1545 and 1550. The processor of the oximeter probe performs thiscalculation. Specifically, the angle changes Δθ and ΔΦ have an arbitraryscaling that is corrected so that the scaling is for blood. Thecorrection can be based on a scaling factor, a correction vector, orboth. The scaling factor, the correction vector, or both can be storedin the nonvolatile memory and remain resident in the memory when theoximeter probe is detached from a power source (e.g., the batteries areremoved from the probe). These values may be generated when the oximeteris first manufactured and tested for use. The values are retrieved fromthe memory and loaded into the processor for use. The correction vectorcan be vector in angle space used by the processor to correct thevectors in angle space or correct the angle changes Δθ and ΔΦ in anglespace.

The correction vector is determined using a tissue phantom. The tissuephantom can be a liquid tissue phantom, one or more rigid tissuephantoms, or a combination of liquid and rigid tissue phantoms. Theoximeter probe makes oxygen saturation measurements on the tissuephantom during a period of time when the tissue phantom has an initialblood oxygenation saturation of 100 percent (e.g., fully oxygenated) andlowers to 0 percent (e.g., fully deoxygenated).

The reflectance data (e.g., for 2, 3, 4, or more wavelengths of light,such as IR) that is generated by the oximeter probe for the tissuephantom is fit to the simulated reflectance curves to determine one ormore simulated reflectance curves that best fits the reflectance data.The absorption coefficients associated with the one or more simulatedreflectance curves are determined. First and second derivatives of thecurves (e.g., spectrum) for the absorption coefficient are determined.

FIG. 21A shows a graph 2100 for the absorption coefficients (e.g.,spectrum) for the fully oxygenated measurements and a graph 2105 for theabsorption coefficients for the fully deoxygenated measurements. FIG.21B shows a first plot 2110 on graph for the first derivatives of thefully oxygenated spectrum with respect to wavelength and a second plot2115 on the graph for the first derivative with respect to wavelength ofthe fully deoxygenated spectrum. FIG. 21C shows a first plot 2120 on agraph for the second derivative with respect to wavelength of the fullyoxygenated spectrum and a second plot 1225 on the graph for the secondderivative with respect to wavelength of the fully deoxygenatedspectrum.

Thereafter, the angular deviations (e.g., θ₁ onto Φ₁) for the curves forthe fully oxygenated measurements are determined for the samewavelengths (e.g., θ₁ angular deviation between line from 760 nanometersto 810 nanometers and line from 810 nanometers to 845 nanometers, and Φ₁angular deviation between line from 810 nanometers to 845 nanometers andline from 845 nanometers and 890 nanometers) as the first and secondtarget tissue measurements described above.

The angular deviations (e.g., θ₂ onto Φ₂) for the curves for the fullydeoxygenated measurement are determined for the same wavelengths (e.g.,θ₂ angular deviation between line from 760 nanometers to 810 nanometersand line from 810 nanometers to 845 nanometers, and Φ₂ angular deviationbetween line from 810 nanometers to 845 nanometers and line from 845nanometers and 890 nanometers) as the first and second target tissuemeasurements described above.

FIG. 22 shows the vector (Δθ, ΔΦ) in angle space where Δθ and ΔΦ areplotted against each other. The delta angles can be used for scaling (orcalibrating) tissue measurements for first and second target tissue(e.g., contralateral breast tissue measurements).

These angular changes Δθ=θ₁−θ₂ and ΔΦ=Φ₁−Φ₂ are determined by theprocessor. The delta angle represents the change in the curvature of theabsorption spectra between the fully deoxygenated measurement and thefully deoxygenated measurements. The delta angles ΔΦ and ΔΦ indicatewhat a 100 percent change in oxygenation for tissue is expected to looklike and provides a reference that other smaller changes in delta anglesΔθ and ΔΦ (e.g., for contralateral breast tissue) can be corrected by toscale arbitrary scaled Δθ and ΔΦ (e.g., for contralateral breasttissue).

The calculated vector (Δθ, ΔΦ) for the tissue phantom is multiplied by acorrection factor to correct for the difference in blood volume in thephantom and blood volume in patient tissue. The correction factor can be10 or other factor to account for a different between blood volume 10percent in the particular phantom used and 1 percent blood volume (orother percentage of blood volume 1.25 percent, 1.2 percent, 1.15percent, 1.1 percent, 1.05 percent, 0.95 percent, 0.9 percent, 0.85percent, 0.8 percent, or other values) for patient tissue.Alternatively, the correction factor can be applied to the measurementsfor the patient tissue as compared to the measurements for the phantom.

FIG. 23 shows the baseline corrected vector 2401 and the calculatedvector corrected 2403 for the phantom corrected by the scaling factorfor the difference in blood volume between the blood volume for thephantom and patient tissue. The delta angles Δθ and ΔΦ corrected forblood volume difference indicate what a 100 percent change inoxygenation for tissue is expected to look like and provides a referencethat other smaller changes in delta angles Δθ and ΔΦ for patient tissue(e.g., for contralateral breast tissue) can be corrected by to scale thearbitrary scaled Δθ and ΔΦ for patient tissue.

In an implementation, the vector for patient tissue is scaled by thevector for the phantom by projecting the vector for the patient tissueonto the vector for the phantom vector. FIG. 24 shows the shows thevector 2501 for patient tissue projected onto the vector 2503 for thephantom. The result of the projection is labeled with reference number2505.

In an implementation, the vector for patient tissue is scaled by thevector for the phantom (1550 of FIG. 15 ) dividing the normalized vectorfor the patient tissue by the normalized vector for the phantom (e.g.,determining a percentage difference) and multiplying by 100 percent and−1.

${\Delta\;{SO}_{2}} = {\left( {- 1} \right)*\frac{{norm}({projectedVector})}{{norm}({BVcorrectedCalibrationVector})}*100\%}$

The factor −1 represents a measurement for a decrease in oxygensaturation of the patient tissue measured by the oximeter probe. In theexample of FIG. 24 , the relative increase in deoxygenation (e.g.,decrease in oxygenation) between the contralateral target tissue of thepatient is approximately 18 percent.

In an implementation, nonlinear transforms are used by the oximeterprobe for scaling the vector (Δθ, ΔΦ) for the patient tissue by thevector (Δθ, ΔΦ) for the phantom.

In an implementation, the oximeter probe transmits light from at leastone of the light source (e.g., source structures) of the oximeter probeinto a first tissue (first breast tissue) at a first location to bemeasured.

The first tissue comprises a first melanin component, such as a firstmelanin content. The first melanin component includes eumelanin,pheomelanin, or both eumelanin, pheomelanin. A number of the detectorstructures receives the light subsequent to transmission through orreflectance from the first tissue.

The received light comprises a first melanin absorption component due tothe first melanin component. That is the received light includesinformation for the melanin in the first tissue as the melanin absorbs aportion of the light transmitted into the first tissue.

The oximeter probe there after determines a melanin compensationcomponent (e.g., an angle correction (such as θ₁, θ₂, Φ₁, Φ₂, Δθ, ΔΦ, orany combination of these), an absorption coefficient determined fromfitting reflectance data to the simulated reflectance curves, anypreliminary, any intermediary, any final calculation result, or anycombination of these) for a melanin absorption component due to amelanin component of tissue.

The melanin absorption component includes the first melanin component.The melanin component includes the first melanin component. The oximeterprobe uses the melanin compensation component to obtain amelanin-corrected oxygen saturation value for the first tissue. Themelanin-corrected oxygen saturation value accounts for the melaninabsorption component.

In an implementation, a method includes contacting a probe tip of anoximeter probe to a first target tissue of a patient, where the firsttarget tissue is healthy tissue; using the oximeter probe, making afirst oximetry measurement on the first target tissue; determining, by aprocessor of the oximeter probe a first plurality of absorptioncoefficients that are dependent on a plurality of wavelengths of lightemitted from the oximeter probe into the first target tissue when themeasurement on the first target tissue is performed; contacting theprobe tip to a second target tissue of the patient, where the secondtarget tissue is tissue for which an oximetry saturation value is to bedetermined; using the oximeter probe, making a second oximetrymeasurement on the second target tissue; determining, by the processorof the oximeter probe a second plurality of absorption coefficients thatare dependent on the first plurality of wavelengths of light emittedfrom the oximeter probe into the second target tissue when themeasurement on the second target tissue is performed; calculating, bythe processor, a first angular deviation and a second angular deviationof a curve for the first plurality of absorption coefficients for thefirst target tissue; calculating, by the processor, a third angulardeviation and a fourth angular deviation of a curve for the secondplurality of absorption coefficients for the second target tissue;calculating, by the processor, a first angular difference between thefirst and second angular deviations and a second angular differencebetween the third and fourth angular deviations; calculating, by theprocessor, a relative change in oxygen saturation between the first andsecond target tissues based on the first and second angular differences;and displaying, by a display of the oximeter probe, a value for therelative oxygen saturation.

The method can include transmitting first light from a source structureof the oximeter probe into the first target tissue; detecting firstreflected light that is reflected from the first target tissue by aplurality of detector structures of the oximeter probe; generating bythe detector structures first reflectance data for the first reflectedlight detected by the detector structures; fitting the reflectance datato a plurality of simulated reflectance curves; determining one or morebest fitting ones of the simulated reflectance curves from the fit ofthe first reflectance data to the plurality of simulated reflectancecurves, where each of the simulated reflectance curves is associatedwith a value for an absorption coefficient; and determining the firstplurality of absorption coefficients for the one or more best fittingones of the simulated reflectance curves to the first reflectance data.

The method can include transmitting second light from the sourcestructure of the oximeter probe into the second target tissue; detectingsecond reflected light that is reflected from the second target tissueby the plurality of detector structures of the oximeter probe;generating by the detector structures second reflectance data for thesecond reflected light detected by the detector structures; fitting thesecond reflectance data to the plurality of simulated reflectancecurves; determining one or more best fitting ones of the simulatedreflectance curves from the fit of the second reflectance data to theplurality of simulated reflectance curves; and determining the secondplurality of absorption coefficients for the one or more best fittingones of the simulated reflectance curves to the second reflectance data.

The method can include scaling, by the processor, the first and secondangular differences with a scaling vector, where the scaling vectorrepresenting a 100 percent difference in oxygenation of a tissuephantom. The scaling includes projecting a first vector comprising datapoints for the first and second angular differences in angle space ontothe scaling vector in angle space. The scaling alternatively includesdividing a normalization of the first vector, that comprises data pointsfor the first and second angular differences in angle space, by anormalization of the scaling vector.

The method can includes calculating, by the processor, a percentagedifference of a quotient of the normalization of the first vectordivided by the normalization of the scaling vector; and the quotient bynegative one to include a decreasing in oxygenation between the firsttarget tissue and the second target tissue. The value displayed on thedisplay is the product of the quotient multiplied by negative one.

In an implementation, a system implements the method where the systemincludes an oximeter probe that includes a handheld housing; a processorhoused in the handheld housing; a memory, housed in the handheldhousing, electronically coupled to the processor and storing first codefor controlling the processor; a display, accessible from an exterior ofthe handheld housing, electronically coupled to the processor; and abattery, housed in the handheld housing, coupled to and supplies powerto the processor, the memory, and the display, where the code includesinstruction executable by the processor executes steps for the methodincluding making a first oximetry measurement on a first target tissueof a patient; determining a first plurality of absorption coefficientsthat are dependent on a plurality of wavelengths of light emitted fromthe oximeter probe into the first target tissue when the measurement onthe first target tissue is performed; making a second oximetrymeasurement on a second target tissue of the patient; determining asecond plurality of absorption coefficients that are dependent on thefirst plurality of wavelengths of light emitted from the oximeter probeinto the second target tissue when the measurement on the second targettissue is performed; calculating a first angular deviation and a secondangular deviation of a curve for the first plurality of absorptioncoefficients for the first target tissue; calculating a third angulardeviation and a fourth angular deviation of a curve for the secondplurality of absorption coefficients for the second target tissue;calculating a first angular difference between the first and secondangular deviations and a second angular difference between the third andfourth angular deviations; calculating a relative change in oxygensaturation between the first and second target tissues based on thefirst and second angular differences; and displaying on the value forthe relative oxygen saturation.

In an implementation a method includes contacting a probe tip to a firsttarget tissue of a patient, where the first target tissue is healthytissue; using the oximeter probe, making a first oximetry measurement onthe first target tissue; determining, by a processor of the oximeterprobe, a first absorption coefficient based on the first oximetrymeasurement for the first target tissue; contacting the probe tip to asecond target tissue of the patient, where the second target tissue istissue for which an oximetry saturation value is to be determined; usingthe oximeter probe, making a second oximetry measurement on the secondtarget tissue; determining, by the processor of the oximeter probe asecond absorption coefficient that is based on the second oximetrymeasurement for the second target tissue; generating, by the processor,a third absorption coefficient by adjusting the second absorptioncoefficient using first absorption coefficient; determining a value foroxygen saturation for the second target tissue from the third absorptioncoefficient; and displaying the value for the oxygen saturation for thesecond target tissue. The method can includes fitting first reflectancedata for the first oximetry measurement to a plurality of simulatedreflectance curves for determining by the processor the first absorptioncoefficient based on the first oximetry measurement for the first targettissue, where the simulated reflectance curves include modeling formelanin in simulated tissue; and determining, by the processor, thefirst absorption coefficient from one or more best fitting one of thesimulated reflectance curves.

This description of the invention has been presented for the purposes ofillustration and description. It is not intended to be exhaustive or tolimit the invention to the precise form described, and manymodifications and variations are possible in light of the teachingabove. The implementations were chosen and described in order to bestexplain the principles of the invention and its practical applications.This description will enable others skilled in the art to best utilizeand practice the invention in various implementations and with variousmodifications as are suited to a particular use. The scope of theinvention is defined by the following claims.

The invention claimed is:
 1. A method comprising: providing a handheld oximeter device comprising a processor, memory, display, battery, and probe tip comprising source structures and detector structures, wherein the memory is coupled to the processor and stores first simulated reflectance curves and second simulated reflectance curves, the display is coupled to the processor, and the source structures and detector structures are coupled to the processor, and the battery is coupled to the processor, memory, and display; from the source structures, emitting light into a tissue to be measured, wherein the tissue to be measured comprises eumelanin and pheomelanin; at the detector structures, receiving light from the tissue in response to the emitted light, wherein the received light comprises a melanin absorption component due to the eumelanin and pheomelanin of the tissue; in the processor, using the received light to calculate an oxygen saturation value for the tissue and determine an indication of a skin color and a melanin content value of the tissue to be measured; using the determined melanin content value to select one of the first simulated reflectance curves or the second simulated reflectance curves stored in the memory by comparing the determined indication of the skin color against the melanin content value associated with each of the simulated reflectance curves and the determined indication of the skin color and the calculated oxygen saturation value, obtaining a melanin-corrected oxygen saturation value for the tissue, wherein the melanin-corrected oxygen saturation value accounts for the melanin absorption component; and displaying the melanin-corrected oxygen saturation value on the display.
 2. The method of claim 1 wherein the indication of a skin color differentiates between two levels of skin color.
 3. The method of claim 2 wherein the handheld oximeter device comprises an input interface, and the input interface is used for the providing to the oximeter device an indication of the skin color of the tissue to be measured.
 4. A method comprising: providing an oximeter device comprising a probe tip comprises source structures and detector structures on a distal end of the device, a processor, and a display proximal to the probe tip and coupled to the processor, wherein the processor of the oximeter device calculates a melanin-corrected oxygen saturation value, and displays the melanin-corrected oxygen saturation value on the display; using the probe tip to make a first measurement and a second measurement to determine the melanin-corrected oxygen saturation value; receiving first information based on the first measurement of a first tissue at a first location when the probe tip is positioned on the first tissue, wherein the melanin-corrected oxygen saturation value is unavailable for display after the first measurement is made and before the second measurement is made; receiving second information based on the second measurement of a second tissue at a second location when the probe tip is positioned on the second tissue, wherein the second location is different from the first location; and using the first information and second information to determine the melanin-corrected oxygen saturation value, wherein the melanin-corrected oxygen saturation value takes into account melanin components of the first tissue and second tissue, and the melanin components comprise eumelanin and pheomelanin.
 5. The method of claim 4 wherein the first location is at a first position of the body, the second location is at a second position of the body, and the first position and second position are contralateral with respect to each other.
 6. The method of claim 4 wherein the oximeter device is a handheld oximeter comprising a power source and an electronic processor housed within an enclosure that also houses the source structures and detector structures of the probe tip.
 7. The method of claim 4 wherein the oximeter device comprises a memory, and the memory stores first simulated reflectance curves for a first melanin content value, second simulated reflectance curves for a second melanin content value, and the first melanin content value is different from the second melanin content value.
 8. The method of claim 7 comprising: based on the first and second information, determining a melanin content value for the first tissue and second tissue; and using the determined melanin content value to select one of the first simulated reflectance curves or the second simulated reflectance curves stored in the memory by comparing the determined melanin content value against the melanin content value associated with each of the simulated reflectance curves.
 9. A method comprising: transmitting light from a light source of an oximeter probe into a first tissue at a first location to be measured, wherein the first tissue comprises a first melanin component, and the first melanin component comprises at least one of eumelanin or pheomelanin; receiving light at a detector of the oximeter probe that is reflected by the first tissue in response to the transmitted light, wherein the received light comprises a first melanin absorption component due to the first melanin component; via a user-input interface of the oximeter probe, receiving an indication of a skin color of the first tissue; using the received indication of the skin color, determining a melanin compensation component for a melanin absorption component due to a melanin component of tissue, wherein the melanin absorption component comprises the first melanin component; and using the melanin compensation component, obtaining a melanin-corrected oxygen saturation value for the first tissue, wherein the melanin-corrected oxygen saturation value accounts for the melanin absorption component.
 10. The method of claim 9 wherein the indication of a skin color differentiates between at least two different levels of skin color.
 11. The method of claim 9 wherein user-input interface comprises a button for a user to select a level of skin color to input as the indication of a skin color.
 12. The method of claim 9 wherein the oximeter device is a handheld oximeter device comprising the light source, the detector, a processor, batteries, and the user-input interface, and the user-input interface is used to provide to the oximeter device the indication of the skin color of the first tissue.
 13. A method comprising: providing a handheld oximeter device comprising a processor, memory, display, battery, and probe tip comprising source structures and detector structures, wherein the memory is coupled to the processor and stores first simulated reflectance curves and second simulated reflectance curves, the display is coupled to the processor, and the source structures and detector structures are coupled to the processor, and the battery is coupled to the processor, memory, and display; from the source structures, emitting light into a tissue to be measured, wherein the tissue to be measured comprises eumelanin and pheomelanin; at the detector structures, receiving light from the tissue in response to the emitted light, wherein the received light comprises a melanin absorption component due to the eumelanin and pheomelanin of the tissue; in the processor, using the received light to calculate an oxygen saturation value for the tissue and to determine an indication of a melanin content value of the tissue to be measured; using the melanin content value to select one of the first simulated reflectance curves or the second simulated reflectance curves stored in the memory by comparing the melanin content value associated with each of the simulated reflectance curves to obtain a melanin-corrected oxygen saturation value for the tissue, wherein the melanin-corrected oxygen saturation value accounts for the melanin absorption component; and displaying the melanin-corrected oxygen saturation value on the display.
 14. The method of claim 13 comprising in the processor, using the received light to determine an indication of a skin color of the tissue to be measured; and using the selected one of the first simulated reflectance curves or the second simulated reflectance curves and the determined indication of the skin color and the calculated oxygen saturation value, to obtain the melanin-corrected oxygen saturation value for the tissue.
 15. The method of claim 14 wherein the indication of a skin color differentiates between two levels of skin color.
 16. The method of claim 14 wherein the handheld oximeter device comprises an input interface, and the input interface is used for the receiving at the oximeter device an indication of the melanin content value of the tissue to be measured.
 17. A method comprising: providing a handheld oximeter device comprising a processor, memory, display, battery, and probe tip comprising source structures and detector structures, wherein the memory is coupled to the processor and stores first simulated reflectance curves and second simulated reflectance curves, the display is coupled to the processor, and the source structures and detector structures are coupled to the processor, and the battery is coupled to the processor, memory, and display; from the source structures, emitting light into a tissue to be measured, wherein the tissue to be measured comprises eumelanin and pheomelanin; at the detector structures, receiving light from the tissue in response to the emitted light, wherein the received light comprises a melanin absorption component due to the eumelanin and pheomelanin of the tissue; using a melanin content value for the tissue to be measured to select one of the first simulated reflectance curves or the second simulated reflectance curves stored in the memory by comparing the melanin content value associated with each of the simulated reflectance curves to obtain an oxygen saturation value for the tissue, wherein the oxygen saturation value accounts for the melanin absorption component; and displaying the oxygen saturation value on the display.
 18. The method of claim 17 wherein the handheld oximeter device comprises an input interface, and the input interface is used for the receiving at the oximeter device an indication of the melanin content value of the tissue to be measured.
 19. The method of claim 17 comprising in the processor, using the received light to determine an indication of the melanin content value of the tissue to be measured. 